- `*Model`s use the `methods.*.predict()` functions to predict demand
given an order time series generated by `timify.OrderHistory`
- `models.base.ForecastingModelABC` unifies how all `*Model`s work
and implements a caching strategy
- implement three `*Model`s for tactical forecasting, based on the
hets, varima, and rtarima models described in the first research paper
- add overall documentation for `urban_meal_delivery.forecasts` package
- move the fixtures in `tests.forecasts.timify.conftest` to
`tests.forecasts.conftest` and adjust the horizon of the test horizon
from two to three weeks
- the function implements a forecasting "method" similar to the
seasonal naive method
=> instead of simply taking the last observation given a seasonal lag,
it linearly extrapolates all observations of the same seasonal lag
from the past into the future; conceptually, it is like the
seasonal naive method with built-in smoothing
- the function is tested just like the `arima.predict()` and
`ets.predict()` functions
+ rename the `tests.forecasts.methods.test_ts_methods` module
into `tests.forecasts.methods.test_predictions`
- re-organize some constants in the `tests` package
- streamline some docstrings
- move the module
- unify the corresponding tests in `tests.forecasts.methods` sub-package
- make all `predict()` and the `stl()` function round results
- streamline documentation
- ensure a `Restaurant` only has one unique `Order.pickup_address`
- rework `Grid.gridify()` so that only pickup addresses are assigned
into `Pixel`s
- include database migrations to ensure the data adhere to these
tighter constraints
- add `.low80`, `.high80`, `.low95`, and `.high95` columns
- add check contraints for the confidence intervals
- rename the `.method` column into `.model` for consistency
- add a Jupyter notebook that allows to install all project-external
dependencies regarding R and R packages
- adjust the GitHub Action workflow to also install R and the R packages
used within the project
- add a `init_r` module that initializes all R packages globally
once the `urban_meal_delivery` package is imported
- the main purpose of this class is to manage querying the order totals
from the database and slice various kinds of time series out of the
data
- the class holds the former `aggregate_orders()` function as a method
- modularize the corresponding tests
- add `tests.config` with globals used when testing to provide a
single source of truth for various settings
- the function queries the database and aggregates the ad-hoc orders
by pixel and time steps into a demand time series
- implement "heavy" integration tests for `aggregate_orders()`
- make `pandas` a package dependency
- streamline the `Config`
- reorganize `urban_meal_delivery.console` into a sub-package
- move `tests.db.conftest` fixtures into `tests.conftest`
=> some integration tests regarding CLI scripts need a database
- add `urban_meal_delivery.console.decorators.db_revision` decorator
to ensure the database is at a certain state before a CLI script runs
- refactor the `urban_meal_delivery.db.grids.Grid.gridify()` constructor:
- bug fix: even empty `Pixel`s end up in the database temporarily
=> create `Pixel` objects only if an `Address` is to be assigned
to it
- streamline code and docstring
- add further test cases
- the purpose of this constructor method is to generate all `Pixel`s
for a `Grid` that have at least one `Address` assigned to them
- fix missing `UniqueConstraint` in `Grid` class => it was not possible
to create two `Grid`s with the same `.side_length` in different cities
- change the `City.viewport` property into two separate `City.southwest`
and `City.northeast` properties; also add `City.total_x` and
`City.total_y` properties for convenience
- the old `UTMCoordinate` class becomes the new `Location` class
- its main purpose is to represent locations in both lat-long
coordinates as well as in the UTM system
- remove `Address.__init__()` and `City.__init__()` methods as they
are not executed for entries retrieved from the database
- simplfiy the `Location.__init__()` => remove `relative_to` argument
- remove the factory functions for creating engines and sessions
- define global engine, connection, and session objects to be used
everywhere in the urban_meal_delivery package
- add Grid, Pixel, and AddressPixelAssociation ORM models
- each Grid belongs to a City an is characterized by the side_length
of all the square Pixels contained in it
- Pixels aggregate Addresses => many-to-many relationship (that is
modeled with SQLAlchemy's Association Pattern to implement a couple
of constraints)
- the Address.x and Address.y properties use the UTMCoordinate class
behind the scenes
- x and y are simple coordinates in an x-y plane
- the (0, 0) origin is the southwest corner of Address.city.viewport
- the class is a utility to abstract working with latitude-longitude
coordinates in their UTM representation (~ "cartesian plane")
- the class's .x and .y properties enable working with simple x-y
coordinates where the (0, 0) origin is the lower-left of a city's
viewport
- create `*Factory` classes with fakerboy and faker that generate
randomized instances of the ORM models
- add new pytest marker: "db" are the integration tests involving the
database whereas "e2e" will be all other integration tests
- streamline the docstrings in the ORM models
